Association Rules Optimization Algorithm Based on Fuzzy Clustering

Abstract

Frequent pattern mining has been an important research direction in association rules. This paper use a methodology by preprocessing the original dataset using fuzzy clustering which can mapped quantitative datasets into linguistic datasets. Then we propose a algorithm based on fuzzy frequent pattern tree for extracting fuzzy frequent itemset from mapped linguistic datasets. Experimental results show that our algorithm is shorter than the F-Apriori on computing time to huge database. For large database, the algorithm presented in this paper is proved to have a good prospect.